Automated segmentation of brain tumour from multimodal MR images is pivotal for the
analysis and monitoring of disease progression. As gliomas are malignant and …

Brain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The
abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to …

Magnetic resonance imaging is the most generally utilized imaging methodology that
permits radiologists to look inside the cerebrum using radio waves and magnets for tumor …

The accessibility and potential of deep learning techniques have increased considerably
over the past years. Image segmentation is one of the many fields which have seen novel …

Deep learning (DL) models have provided state-of-the-art performance in various medical
imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) …

Accurate segmentation of brain tumors is crucial for diagnostic evaluation and clinical
planning. Convolutional-based and Transformer-based models have shown promising …

The ability to accurately diagnose and analyze head and neck (H&N) tumors in head and
neck cancer (HNC) is critical in the administration of patient specific radiation therapy …

This work focuses on training a deep learning network in a federated learning framework.
The Federated Tumor Segmentation Challenge has 2 separate tasks. Task-1 was to design …

Background Invasive coronary angiography (ICA) is a primary imaging modality that
visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In …

Blood cancer can only be diagnosed properly if it is detected early. Each year, more than
1.24 million new cases of blood cancer are reported worldwide. There are about 6,000 …